Silver‐Catalyzed Reduction and Oxidation of Aldehydes and Their Derivatives
Bibliographic record
Abstract
Alcohol, aldehyde, and carboxylic acid are closely related families of chemicals. These chemicals, and their derivatives, are vastly useful in almost all aspect of modern chemical industry, producing solvents, fuels, active pharmaceutical ingredients (APIs), preservatives. In the last few decades, homogeneous silver-catalyzed reactions have seen important developments. This chapter reviews the silver-catalyzed reduction of aldehyde, including hydrosilylation, hydrogenation, and transfer hydrogenation. In 2014, Li and coworkers reported a simple, efficient, and chemoselective silver-catalyzed transfer hydrogenation of aldehydes 20 into alcohols 21 in air and water for the first time by using formate as a convenient source of hydrogen. The use of AgF-DavePhos as catalyst generated a selective reduction of aromatic aldehydes, whereas both aromatic 20 and aliphatic aldehydes 22 were reduced efficiently to 21 and 23 with AgF-BrettPhos and AgF-SPhos catalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".